Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target, and answering queries over target instances in a way that is semantically consistent with the information in the source. Theoretical foundations of data exchange have been actively explored recently. It was also noticed that the standard certain answers semantics may behave in very odd ways. In this paper I

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... that this behavior is due to the fact that the presence of incomplete information in target instances has been ignored; in particular, proper query evaluation techniques for databases with nulls have not been used, and the distinction between closed and open world semantics has not been made. I present a concept of target solutions based on the closed world assumption, and show that the space of all solutions has two extreme points: the canonical universal solution and the core, well studied in data exchange. I show how to define semantics of query answering taking into account incomplete information, and show that the well-known anomalies go away with the new semantics. The paper also contains results on the complexity of query answering, upper approximations to queries (maybe-answers), and various extensions. by Fagin, Kolaitis, Miller, and Popa [10, 11] . A survey of the area was presented in the most recent PODS invited talk [18] . Here we revisit the basics of relational data exchange, as described in [10, 11, 18] . A data exchange setting is a triple (σ, τ, Σ) where σ and τ are source and target schemas (relational vocabularies), and Σ is a set of source-to-target dependencies (STDs),